CMSC 471 Fall 2009 Temporal Difference Learning

1. CMSC 471Fall 2009Temporal Difference Learning Prof. Marie desJardinsClass #25 – Tuesday, 11/24Thanks to Rich Sutton and Andy Barto for the use of their slides! R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

2. Chapter 6: Temporal Difference Learning • Introduce Temporal Difference (TD) learning • Focus first on policy evaluation, or prediction, methods • Then extend to control methods Objectives of this chapter: R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

3. T T T T T T T T T T T T T T T T T T T T Simple Monte Carlo R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

4. TD Prediction Policy Evaluation (the prediction problem): for a given policy p, compute the state-value function Recall: target: the actual return after time t target: an estimate of the return R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

5. T T T T T T T T T T T T T T T T T T T T Simplest TD Method R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

6. T T T T T T T T T T cf. Dynamic Programming T T T R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

7. TD Bootstraps and Samples • Bootstrapping: update involves an estimate • MC does not bootstrap • DP bootstraps • TD bootstraps • Sampling: update does not involve an expected value • MC samples • DP does not sample • TD samples R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

8. Example: Driving Home R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

9. Driving Home • Changes recommended by Monte Carlo methods (a=1) • Changes recommended • by TD methods (a=1) R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

10. Advantages of TD Learning • TD methods do not require a model of the environment, only experience • TD, but not MC, methods can be fully incremental • You can learn before knowing the final outcome • Less memory • Less peak computation • You can learn without the final outcome • From incomplete sequences • Both MC and TD converge (under certain assumptions to be detailed later), but which is faster? R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

11. Random Walk Example • Values learned by TD(0) after • various numbers of episodes R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

12. TD and MC on the Random Walk • Data averaged over • 100 sequences of episodes R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

13. Optimality of TD(0) Batch Updating: train completely on a finite amount of data, e.g., train repeatedly on 10 episodes until convergence. Compute updates according to TD(0), but only update estimates after each complete pass through the data. For any finite Markov prediction task, under batch updating, TD(0) converges for sufficiently small a. Constant-a MC also converges under these conditions, but to a difference answer! R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

14. Random Walk under Batch Updating • After each new episode, all previous episodes were treated as a batch, and algorithm was trained until convergence. All repeated 100 times. R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

15. You are the Predictor Suppose you observe the following 8 episodes: A, 0, B, 0 B, 1 B, 1 B, 1 B, 1 B, 1 B, 1 B, 0 R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

16. You are the Predictor R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

17. You are the Predictor • The prediction that best matches the training data is V(A)=0 • This minimizes the mean-square-error on the training set • This is what a batch Monte Carlo method gets • If we consider the sequentiality of the problem, then we would set V(A)=.75 • This is correct for the maximum likelihood estimate of a Markov model generating the data • i.e, if we do a best fit Markov model, and assume it is exactly correct, and then compute what it predicts (how?) • This is called the certainty-equivalence estimate • This is what TD(0) gets R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

18. Learning An Action-Value Function R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

19. Sarsa: On-Policy TD Control Turn this into a control method by always updating the policy to be greedy with respect to the current estimate: R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

20. Windy Gridworld undiscounted, episodic, reward = –1 until goal R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

21. Results of Sarsa on the Windy Gridworld R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

22. Q-Learning: Off-Policy TD Control R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

23. Cliffwalking • e-greedy, e = 0.1 R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

24. Actor-Critic Methods • Explicit representation of policy as well as value function • Minimal computation to select actions • Can learn an explicit stochastic policy • Can put constraints on policies • Appealing as psychological and neural models R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

25. Actor-Critic Details R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

26. Dopamine Neurons and TD Error W. Schultz et al. Universite de Fribourg R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

27. Average Reward Per Time Step the same for each state if ergodic R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

28. R-Learning R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

29. Access-Control Queuing Task Apply R-learning • n servers • Customers have four different priorities, which pay reward of 1, 2, 3, or 4, if served • At each time step, customer at head of queue is accepted (assigned to a server) or removed from the queue • Proportion of randomly distributed high priority customers in queue is h • Busy server becomes free with probability p on each time step • Statistics of arrivals and departures are unknown n=10, h=.5, p=.06 R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

30. Afterstates • Usually, a state-value function evaluates states in which the agent can take an action. • But sometimes it is useful to evaluate states after agent has acted, as in tic-tac-toe. • Why is this useful? • What is this in general? R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction

31. Summary • TD prediction • Introduced one-step tabular model-free TD methods • Extend prediction to control by employing some form of GPI • On-policy control: Sarsa • Off-policy control: Q-learning and R-learning • These methods bootstrap and sample, combining aspects of DP and MC methods R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction